Effectiveness of Deep Learning Models in Cybercrime Prediction
Article Sidebar
-
Cybercrime Prediction, Deep Learning
Abstract
The rise of cybercrime poses significant challenges to security agencies and organizations worldwide. Traditional methods of crime prediction often fall short in accurately identifying potential threats. As a result, there is a growing interest in leveraging advanced technologies, such as deep learning, to enhance predictive capabilities in cybersecurity. This research aims to evaluate the effectiveness of deep learning models in predicting cybercrime incidents. The study investigates how these models can improve accuracy and reliability compared to conventional prediction techniques. A dataset comprising historical cybercrime incidents was collected and preprocessed to extract relevant features. Various deep learning architectures, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), were implemented. The models were trained and validated using a portion of the data, while performance metrics such as accuracy, precision, recall, and F1-score were used to assess their predictive capabilities. The findings indicate that deep learning models significantly outperform traditional methods in predicting cybercrime incidents. The best-performing model achieved an accuracy of 92%, showcasing its ability to identify complex patterns in the data. Additionally, deep learning models demonstrated lower false positive rates, enhancing their reliability in real-world applications. The research concludes that deep learning is a powerful tool for predicting cybercrime, offering enhanced accuracy and efficiency. These findings contribute to the field by highlighting the potential of advanced machine learning techniques in improving cybersecurity measures. Future work should focus on refining these models and exploring their applicability in real-time cyber threat detection.
Full text article
References
Adebowale, M. A., Lwin, K. T., & Hossain, M. A. (2023). Intelligent phishing detection scheme using deep learning algorithms. Journal of Enterprise Information Management, 36(3), 747–766. https://doi.org/10.1108/JEIM-01-2020-0036
Afroz, Md., Nyakwende, E., & Goswami, B. (2024). A Hybrid Deep Learning Approach for Accurate Network Intrusion Detection Using Traffic Flow Analysis in IoMT Domain. In S. Das, S. Saha, C. A. Coello Coello, & J. C. Bansal (Eds.), Advances in Data-Driven Computing and Intelligent Systems (Vol. 893, pp. 369–385). Springer Nature Singapore. https://doi.org/10.1007/978-981-99-9518-9_27
Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M. S., Hasan, M., Van Essen, B. C., Awwal, A. A. S., & Asari, V. K. (2019). A State-of-the-Art Survey on Deep Learning Theory and Architectures. Electronics, 8(3), 292. https://doi.org/10.3390/electronics8030292
Alotaibi, F. A., & Mishra, S. (2024). Cyber Security Intrusion Detection and Bot Data Collection using Deep Learning in the IoT. International Journal of Advanced Computer Science and Applications, 15(3). https://doi.org/10.14569/IJACSA.2024.0150343
AlShehri, R. A. M., & Saudagar, A. K. J. (2023). Detecting Threats from Live Videos using Deep Learning Algorithms. International Journal of Advanced Computer Science and Applications, 14(11). https://doi.org/10.14569/IJACSA.2023.0141166
Andersen, R. S., Peimankar, A., & Puthusserypady, S. (2019). A deep learning approach for real-time detection of atrial fibrillation. Expert Systems with Applications, 115, 465–473. https://doi.org/10.1016/j.eswa.2018.08.011
Beaman, C., Barkworth, A., Akande, T. D., Hakak, S., & Khan, M. K. (2021). Ransomware: Recent advances, analysis, challenges and future research directions. Computers & Security, 111, 102490. https://doi.org/10.1016/j.cose.2021.102490
Bhuvaneshwari A J, & P Kaythry. (2023). A Review of Deep Learning Strategies for Enhancing Cybersecurity in Networks. Journal of Scientific & Industrial Research, 82(12). https://doi.org/10.56042/jsir.v82i12.1702
Biswas, B., Mukhopadhyay, A., Kumar, A., & Delen, D. (2024). A hybrid framework using explainable AI (XAI) in cyber-risk management for defence and recovery against phishing attacks. Decision Support Systems, 177, 114102. https://doi.org/10.1016/j.dss.2023.114102
Bojja, S., Rajitha, A., Aravinda, K., Nagpal, A., Kalra, R., & Radi, U. K. (2024). Advanced Machine Learning Techniques for Data Prediction in WSNs. 2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0, 1–6. https://doi.org/10.1109/OTCON60325.2024.10687955
Butt, U. A., Amin, R., Aldabbas, H., Mohan, S., Alouffi, B., & Ahmadian, A. (2023). Cloud-based email phishing attack using machine and deep learning algorithm. Complex & Intelligent Systems, 9(3), 3043–3070. https://doi.org/10.1007/s40747-022-00760-3
Chaganti, R., Ravi, V., & Pham, T. D. (2023). A multi-view feature fusion approach for effective malware classification using Deep Learning. Journal of Information Security and Applications, 72, 103402. https://doi.org/10.1016/j.jisa.2022.103402
Dehghan, F. (2024). A Deep Learning-Based Method for Intrusion Detection in Smart Grid: A Case Study of Distributed Denial of Service Detection. 2024 28th International Electrical Power Distribution Conference (EPDC), 1–5. https://doi.org/10.1109/EPDC62178.2024.10571748
Ferrag, M. A., Maglaras, L., Moschoyiannis, S., & Janicke, H. (2020). Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study. Journal of Information Security and Applications, 50, 102419. https://doi.org/10.1016/j.jisa.2019.102419
Gao, Y., Gao, L., Li, X., & Wang, X. V. (2020). A Multilevel Information Fusion-Based Deep Learning Method for Vision-Based Defect Recognition. IEEE Transactions on Instrumentation and Measurement, 69(7), 3980–3991. https://doi.org/10.1109/TIM.2019.2947800
Hantrais, L., Allin, P., Kritikos, M., Sogomonjan, M., Anand, P. B., Livingstone, S., Williams, M., & Innes, M. (2021). Covid-19 and the digital revolution. Contemporary Social Science, 16(2), 256–270. https://doi.org/10.1080/21582041.2020.1833234
Hassan, J. U., Missen, M. M. S., Firdous, A., Maham, A., & Ikram, A. (2023). An Adaptive M-Learning Usability Model for Facilitating M-Learning for Slow Learners. International Journal of Interactive Mobile Technologies, 17(19), 48–69. Scopus. https://doi.org/10.3991/ijim.v17i19.42153
Kaythry, P. (2023). A Review of Deep Learning Strategies for Enhancing Cybersecurity in Networks. Journal of Scientific & Industrial Research, 82(12). https://doi.org/10.56042/jsir.v82i12.1702
Khaleel, Y. L., Habeeb, M. A., Albahri, A. S., Al-Quraishi, T., Albahri, O. S., & Alamoodi, A. H. (2024). Network and cybersecurity applications of defense in adversarial attacks: A state-of-the-art using machine learning and deep learning methods. Journal of Intelligent Systems, 33(1), 20240153. https://doi.org/10.1515/jisys-2024-0153
Kumar, R., & Bhat, A. (2022). A study of machine learning-based models for detection, control, and mitigation of cyberbullying in online social media. International Journal of Information Security, 21(6), 1409–1431. https://doi.org/10.1007/s10207-022-00600-y
Le, D. K., & Yoon, J. Y. (2023). A hybrid CFD – Deep learning methodology for improving the accuracy of pressure drop prediction in cyclone separators. Chemical Engineering Research and Design, 190, 296–311. https://doi.org/10.1016/j.cherd.2022.12.035
Liu, G., Xia, W., Xu, H., Dai, Y., & Chen, W. (2022). Research on Resilience Cloud Environment Capability Evaluation System Under Complex Cyber Threat. In X. Sun, X. Zhang, Z. Xia, & E. Bertino (Eds.), Artificial Intelligence and Security (Vol. 13339, pp. 427–437). Springer International Publishing. https://doi.org/10.1007/978-3-031-06788-4_36
M, G., & Vidhya, A. (2023). Optimized Hybrid Model Using Machine Learning to Combat the Prevalence of Cybercrime. 2023 7th International Conference on Electronics, Communication and Aerospace Technology (ICECA), 739–746. https://doi.org/10.1109/ICECA58529.2023.10395218
Mutescu, P. M., Lavric, A., Petrariu, A. I., & Popa, V. (2023). A Hybrid Deep Learning Spectrum Sensing Architecture for IoT Technologies Classification. 2023 17th International Conference on Engineering of Modern Electric Systems (EMES), 1–4. https://doi.org/10.1109/EMES58375.2023.10171667
Näsi, M., Danielsson, P., & Kaakinen, M. (2023). Cybercrime Victimisation and Polyvictimisation in Finland—Prevalence and Risk Factors. European Journal on Criminal Policy and Research, 29(2), 283–301. https://doi.org/10.1007/s10610-021-09497-0
Noguchi, Y., Tachi, T., & Teramachi, H. (2021). Detection algorithms and attentive points of safety signal using spontaneous reporting systems as a clinical data source. Briefings in Bioinformatics, 22(6), bbab347. https://doi.org/10.1093/bib/bbab347
Ozcanli, A. K., Yaprakdal, F., & Baysal, M. (2020). Deep learning methods and applications for electrical power systems: A comprehensive review. International Journal of Energy Research, 44(9), 7136–7157. https://doi.org/10.1002/er.5331
Ravi, V., & Chaganti, R. (2023). EfficientNet deep learning meta-classifier approach for image-based android malware detection. Multimedia Tools and Applications, 82(16), 24891–24917. https://doi.org/10.1007/s11042-022-14236-6
Sai Meghana, G. V., Saqlain Afroz, S., Gurindapalli, R., Katari, S., & Swetha, K. (2024). A Survey paper on Understanding the Rise of AI-driven Cyber Crime and Strategies for Proactive Digital Defenders. 2024 4th International Conference on Pervasive Computing and Social Networking (ICPCSN), 25–30. https://doi.org/10.1109/ICPCSN62568.2024.00012
Sharma, P., Prakash, S., & Chaudhary, K. (2023). Analyzing Cybersecurity Patterns in the Pacific Region: Trends and Challenges for 2023. 2023 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), 1–7. https://doi.org/10.1109/CSDE59766.2023.10487141
Shaukat, K., Luo, S., Chen, S., & Liu, D. (2020). Cyber Threat Detection Using Machine Learning Techniques: A Performance Evaluation Perspective. 2020 International Conference on Cyber Warfare and Security (ICCWS), 1–6. https://doi.org/10.1109/ICCWS48432.2020.9292388
Shekokar, N. M., Vasudevan, H., Durbha, S. S., Michalas, A., & Nagarhalli, T. P. (Eds.). (2024). Intelligent approaches to cyber security (First edition). CRC Press, Taylor & Francis Group. https://doi.org/10.1201/9781003408307
Singh, D., Shukla, A., & Sajwan, M. (2021). Deep transfer learning framework for the identification of malicious activities to combat cyberattack. Future Generation Computer Systems, 125, 687–697. https://doi.org/10.1016/j.future.2021.07.015
Sulaiman, S., Kawsara, A., El Sabbagh, A., Mahayni, A. A., Gulati, R., Rihal, C. S., & Alkhouli, M. (2023). Machine learning vs. Conventional methods for prediction of 30-day readmission following percutaneous mitral edge-to-edge repair. Cardiovascular Revascularization Medicine, 56, 18–24. https://doi.org/10.1016/j.carrev.2023.05.013
Tareq, I., Elbagoury, B. M., El-Regaily, S. A., & El-Horbaty, E.-S. M. (2024). Deep Reinforcement Learning Approach for Cyberattack Detection. International Journal of Online and Biomedical Engineering (iJOE), 20(05), 15–30. https://doi.org/10.3991/ijoe.v20i05.48229
Usoh, M., Asuquo, P., Ozuomba, S., Stephen, B., & Inyang, U. (2023). A hybrid machine learning model for detecting cybersecurity threats in IoT applications. International Journal of Information Technology, 15(6), 3359–3370. https://doi.org/10.1007/s41870-023-01367-8
Van De Weijer, S., Leukfeldt, R., & Moneva, A. (2024). Cybercrime during the COVID-19 pandemic: Prevalence, nature and impact of cybercrime for citizens and SME owners in the Netherlands. Computers & Security, 139, 103693. https://doi.org/10.1016/j.cose.2023.103693
Yang, Y., Ren, Z., Lenart, C., Corsello, A., Kosko, K., Su, S., & Guan, Q. (2023). Deep Learning-based Student Learning Behavior Understanding Framework in Real Classroom Scene. 2023 International Conference on Machine Learning and Applications (ICMLA), 437–444. https://doi.org/10.1109/ICMLA58977.2023.00067
Zhang, D., De Leeuw, M., & Verschuur, E. (2021). Deep learning-based seismic surface-related multiple adaptive subtraction with synthetic primary labels. First International Meeting for Applied Geoscience & Energy Expanded Abstracts, 2844–2848. https://doi.org/10.1190/segam2021-3584041.1
Zhang, J., Pan, L., Han, Q.-L., Chen, C., Wen, S., & Xiang, Y. (2022). Deep Learning Based Attack Detection for Cyber-Physical System Cybersecurity: A Survey. IEEE/CAA Journal of Automatica Sinica, 9(3), 377–391. https://doi.org/10.1109/JAS.2021.1004261
Authors
Copyright (c) 2024 Muhammad Mustofa, Shazia Akhtar, Arnes Yuli Vandika
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
